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Latest from AI Agent Research Hub

26 recent articles
AI Agent Research Hub
AI Agent Research Hub
Apr 25, 2026 · Artificial Intelligence

AI Review Pilot at AAAI-26: 22,977 Papers Processed in 24 Hours, Accuracy Outperforms Human Reviewers

The AAAI‑26 AI Review Pilot deployed a multi‑stage GPT‑5‑based system to generate full‑text reviews for 22,977 submissions within a day at a cost of less than $1 per paper, and a large‑scale survey showed reviewers rated the AI feedback higher than human reviews on six of nine quality dimensions.

AAAI-26AIPeer Review
0 likes · 25 min read
AI Review Pilot at AAAI-26: 22,977 Papers Processed in 24 Hours, Accuracy Outperforms Human Reviewers
AI Agent Research Hub
AI Agent Research Hub
Apr 22, 2026 · Artificial Intelligence

Solving the Burgers Equation with TINN: High‑Precision Physics‑Informed Neural Networks in 380 seconds

This tutorial presents the Time‑Induced Neural Network (TINN) framework that overcomes the time‑entanglement issue of standard PINNs by introducing a dedicated time‑subnet with FiLM modulation, employs a Levenberg‑Marquardt optimizer for second‑order updates, and demonstrates a 1e‑6 relative error solution of the 1‑D viscous Burgers equation in just 371 seconds on an RTX 4090.

Burgers EquationFiLM ModulationJAX
0 likes · 21 min read
Solving the Burgers Equation with TINN: High‑Precision Physics‑Informed Neural Networks in 380 seconds
AI Agent Research Hub
AI Agent Research Hub
Apr 16, 2026 · Artificial Intelligence

Conditionally Adaptive Augmented Lagrangian PINNs for Forward and Inverse PDE Solving (CMAME Open‑Source Code)

The article analyzes the multi‑objective loss imbalance in physics‑informed neural networks, introduces the CAPU algorithm that assigns independent adaptive penalty parameters via an RMSProp‑inspired update with a max‑protection rule, and demonstrates its superior accuracy on a range of forward and inverse PDE benchmarks, providing theoretical guarantees and open‑source PyTorch code.

CAPUPDE solvingadaptive penalty
0 likes · 23 min read
Conditionally Adaptive Augmented Lagrangian PINNs for Forward and Inverse PDE Solving (CMAME Open‑Source Code)
AI Agent Research Hub
AI Agent Research Hub
Apr 12, 2026 · Artificial Intelligence

FactReview: An AI‑Agent System for Evidence‑Grounded Peer Review of Papers and Code

FactReview redefines peer review by formalizing it as evidence‑grounded claim assessment, extracting structured statements from papers, locating related literature, and verifying empirical claims through sandboxed code execution, producing a five‑level label report; experiments on CompGCN and backend LLM analyses demonstrate its strengths and current limitations.

AI peer reviewLLMclaim verification
0 likes · 25 min read
FactReview: An AI‑Agent System for Evidence‑Grounded Peer Review of Papers and Code
AI Agent Research Hub
AI Agent Research Hub
Apr 2, 2026 · Artificial Intelligence

Constrained Symbolic Regression and Weak Form Uncover Laws from Noisy Incomplete Data

By integrating universal physical symmetries, weak‑form integral transformations, and sparse symbolic regression, the authors devise a hybrid framework that extracts governing Navier‑Stokes equations from high‑dimensional, noisy, and partially observed fluid experiments, while also reconstructing hidden pressure and Lorentz force fields.

Navier-Stokesfluid dynamicslatent variables
0 likes · 12 min read
Constrained Symbolic Regression and Weak Form Uncover Laws from Noisy Incomplete Data
AI Agent Research Hub
AI Agent Research Hub
Apr 1, 2026 · Artificial Intelligence

Scale‑PINN Solves High‑Re Navier‑Stokes in 100 seconds, Cutting Error by 96 %

The tutorial introduces Scale‑PINN, which adds an evolutionary regularization term inspired by pseudo‑time stepping to the PINN loss, enabling a shared‑backbone network to solve the lid‑driven cavity Navier‑Stokes problem at Re = 7500 in about 100 seconds and reducing the relative velocity error by roughly 96 % compared with a standard PINN.

Evolutionary regularizationHigh Reynolds numberJAX
0 likes · 25 min read
Scale‑PINN Solves High‑Re Navier‑Stokes in 100 seconds, Cutting Error by 96 %
AI Agent Research Hub
AI Agent Research Hub
Mar 24, 2026 · Artificial Intelligence

How PeRCNN Turns Convolution Kernels into Differential Operators for Physics‑Informed Learning

PeRCNN embeds physics directly into its architecture by replacing additive nonlinearities with element‑wise multiplication in Π‑blocks, enabling convolution kernels to act as finite‑difference operators, which yields superior forward and inverse PDE solving, accurate coefficient identification, robust equation discovery, and interpretable models, as demonstrated on multiple reaction‑diffusion benchmarks.

PeRCNNconvolutional neural networkdeep learning
0 likes · 22 min read
How PeRCNN Turns Convolution Kernels into Differential Operators for Physics‑Informed Learning